Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges

The pathophysiology of atrial fibrillation (AF) is broad, with components related to the unique and diverse cellular electrophysiology of atrial myocytes, structural complexity, and heterogeneity of atrial tissue, and pronounced disease-associated remodeling of both cells and tissue. A major challenge for rational design of AF therapy, particularly pharmacotherapy, is integrating these multiscale characteristics to identify approaches that are both efficacious and independent of ventricular contraindications. Computational modeling has long been touted as a basis for achieving such integration in a rapid, economical, and scalable manner. However, computational pipelines for AF-specific drug screening are in their infancy, and while the field is progressing quite rapidly, major challenges remain before computational approaches can fill the role of workhorse in rational design of AF pharmacotherapies. In this review, we briefly detail the unique aspects of AF pathophysiology that determine requirements for compounds targeting AF rhythm control, with emphasis on delimiting mechanisms that promote AF triggers from those providing substrate or supporting reentry. We then describe modeling approaches that have been used to assess the outcomes of drugs acting on established AF targets, as well as on novel promising targets including the ultra-rapidly activating delayed rectifier potassium current, the acetylcholine-activated potassium current and the small conductance calcium-activated potassium channel. Finally, we describe how heterogeneity and variability are being incorporated into AF-specific models, and how these approaches are yielding novel insights into the basic physiology of disease, as well as aiding identification of the important molecular players in the complex AF etiology

Susceptibility to Arrhythmia in the Infarcted Heart Depends on Myofibroblast Density

AbstractFibroblasts are electrophysiologically quiescent in the healthy heart. Evidence suggests that remodeling following myocardial infarction may include coupling of myofibroblasts (Mfbs) among themselves and with myocytes via gap junctions. We use a magnetic resonance imaging-based, three-dimensional computational model of the chronically infarcted rabbit ventricles to characterize the arrhythmogenic substrate resulting from Mfb infiltration as a function of Mfb density. Mfbs forming gap junctions were incorporated into both infarct regions, the periinfarct zone (PZ) and the scar; six scenarios were modeled: 0%, 10%, and 30% Mfbs in the PZ, with either 80% or 0% Mfbs in the scar. Ionic current remodeling in PZ was also included. All preparations exhibited elevated resting membrane potential within and near the PZ and action potential duration shortening throughout the ventricles. The unique combination of PZ ionic current remodeling and different degrees of Mfb infiltration in the infarcted ventricles determines susceptibility to arrhythmia. At low densities, Mfbs do not alter arrhythmia propensity; the latter arises predominantly from ionic current remodeling in PZ. At intermediate densities, Mfbs cause additional action potential shortening and exacerbate arrhythmia propensity. At high densities, Mfbs protect against arrhythmia by causing resting depolarization and blocking propagation, thus overcoming the arrhythmogenic effects of PZ ionic current remodeling

Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges

The pathophysiology of atrial fibrillation (AF) is broad, with components related to the unique and diverse cellular electrophysiology of atrial myocytes, structural complexity, and heterogeneity of atrial tissue, and pronounced disease-associated remodeling of both cells and tissue. A major challenge for rational design of AF therapy, particularly pharmacotherapy, is integrating these multiscale characteristics to identify approaches that are both efficacious and independent of ventricular contraindications. Computational modeling has long been touted as a basis for achieving such integration in a rapid, economical, and scalable manner. However, computational pipelines for AF-specific drug screening are in their infancy, and while the field is progressing quite rapidly, major challenges remain before computational approaches can fill the role of workhorse in rational design of AF pharmacotherapies. In this review, we briefly detail the unique aspects of AF pathophysiology that determine requirements for compounds targeting AF rhythm control, with emphasis on delimiting mechanisms that promote AF triggers from those providing substrate or supporting reentry. We then describe modeling approaches that have been used to assess the outcomes of drugs acting on established AF targets, as well as on novel promising targets including the ultra-rapidly activating delayed rectifier potassium current, the acetylcholine-activated potassium current and the small conductance calcium-activated potassium channel. Finally, we describe how heterogeneity and variability are being incorporated into AF-specific models, and how these approaches are yielding novel insights into the basic physiology of disease, as well as aiding identification of the important molecular players in the complex AF etiology

Response to ectopic stimulation in simulations with 30% I_Na and arrested sinus.

<p>(A–B) Activation sequences and V_m maps showing sustained arrhythmia induction by PVCs originating in two locations. Green arrows: successful wavefront propagation; red arrows: conduction block. (C–E) Activation sequences for three ectopic stimulation sites from which no episodes of sustained reentry were induced. Activation times measured relative to PVC delivery. Time scales vary from panel to panel but spacing between isochrones lines is always 10 <i>ms</i>. V_m: membrane voltage; PVC: premature ventricular contraction.</p

Maps of ventricular WT.

<p>(A–B) Anterior and posterior views. (C–D) Cutaway views of right and left sides of ventricular septum. (E–F) Cutaway views of posterior and anterior endocardium. All 10 endocardial PVC initiation locations are shown. Rectangle color indicates whether ectopic stimuli originating from that site induced sustained arrhythmia. Asterisk (in E) indicates a region of WT expansion in the RV free wall (see text for further detail). WT: wall thickness.</p

Graphical illustration of SF calculation from V_m and I_m traces in a model with 50% I_Na and arrested sinus rhythm.

<p>3 cases are shown: just above threshold (A), at threshold (B), and well-below threshold (C). For a particular excitation interval t_A, the charge necessary to initiate an AP is estimated based on behavior in uncoupled cells. Notably, for the example shown in (C), I_m never crosses below 0. SF: safety factor; V_m: transmembrane voltage; I_m: transmembrane current; I_Na: sodium current; AP: action potential; Q_net: charge available to depolarize the membrane (incoming minus outgoing).</p

Vulnerability to induction of sustained arrhythmia by 310 ectopic stimuli from 10 different PVC sites (E1–E10) at 30% I_Na level and arrested sinus activation.

<p>See text for further detail. Specific simulations with activation patterns and/or V_m maps shown in Figs. 3, 5, or 6 are indicated by white text.</p

Maps of SF for propagation during the first excitation cycle following ectopic stimulation from several different sites in simulations with 30% of normal I_Na.

<p>Thick lines of critical SF (black/red: SF<1) indicate locations of conduction block. (A–B) PVCs analyzed here induced sustained reentry (>1000 <i>ms</i>). Lines of critical SF were present along the septal insertion. (C–E) PVCs analyzed here did not induce reentry. SF: safety factor.</p

Maps of SF for propagation during the first excitation cycle following ectopic stimulation from several different sites in simulations with 40% of normal I_Na (same scale as Fig. 5); location and timing of all 5 PVCs is identical to Fig. 5.

<p>None of the PVCs analyzed here induced reentry. Labels show quantitative comparison to the corresponding SF maps in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086947#pone-0086947-g005" target="_blank">Fig. 5</a>. Vol_SF<1: percent volume of tissue in the ventricles with SF<1. Comparison of Vol_SF<1 to the corresponding values in the simulations with 30% of normal I_Na shown in parentheses.</p